// (C) Copyright 2013, Tianlin Shi (tianlin [at] mit [dot] edu) // The code implements paper : A Fully Polynomial-Time Approximation Scheme for Approximating a Sum of Random Variables // [J. Li, T. Shi, 2013] http://arxiv.org/abs/1303.6071.

// SORV is free software; you can redistribute it and/or modify it under // the terms of the GNU General Public License as published by the Free // Software Foundation; either version 2 of the License, or (at your // option) any later version.

// SORV is distributed in the hope that it will be useful, but WITHOUT // ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or // FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License // for more details.

// You should have received a copy of the GNU General Public License // along with this program; if not, write to the Free Software // Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 // USA

Build & Install: python setup.py build sudo python setup.py install

Usage: in python: import sorvlib the algorithm relies on oracle calls to probability distributions. in python, define functions like

def uniform_dist1(s1, s2): n = 10 res = 0 if(min(s2,n-1) < max(s1, 0)): return 0 return (min(s2,n-1)-max(s1,0)+1)*1.0/n

sorvlib.computeSorv takes three args: func_list, C, epsilon.

func_list is a list all function instances. for example, use: sorvlib.computeSorv([uniform_dist1 uniform_dist1], 3, 0.1) to compute 1 \pm 0.1 approximation of the probability such that the sum of two uniform distributed variables is no larger than 3.